README.md

metadata

title: Math Conjecture Trainer
sdk: gradio
sdk_version: 6.6.0
python_version: '3.10'
app_file: app.py
pinned: false
emoji: 🧮

Math Conjecture Trainer Space

An autonomous training operations console for DeepSeek-Math that runs multi-stage curriculum fine-tuning on Space GPU, executes post-training quality evaluation, and publishes only qualified adapters, checkpoints, and run reports to your Hugging Face model repository.

This Space is the tactical operations console for maths-conjuncture-solutions and is wired to:

• dataset: NorthernTribe-Research/math-conjecture-training-corpus
• model repo: NorthernTribe-Research/math-conjecture-model

End-to-end flow

1. Download released parquet splits (train/validation/test).
2. Build runtime config from configs/deepseek_math_sota.yaml.
3. Run 4-stage curriculum LoRA fine-tuning with scripts/train_sota.py.
4. Run post-train evaluation (pass@1, pass@k, exact/boxed, family metrics).
5. Apply quality gate thresholds before hub push.
6. Emit training_summary.json + post_eval_report.json and stream live telemetry in UI.

Access posture

Credentials and publish permissions are handled by deployment runtime settings.

Runtime controls

• Autonomous Mode: enabled by default; applies full-stage training/eval/gate/publish profile automatically.
• Run Evaluation After Training: toggles post-train eval in runtime config.
• Enforce Quality Gate: enables/disables promotion gate checks.
• Gate Min pass@1, Gate Min pass@k, Gate Min Rows: runtime gate thresholds.
• Live Tactical Telemetry: real-time stage progression, runtime posture, and training-loss graph (sparkline) with gate/push state.
• Ops Console (Live Log + Mission JSON): unified panel for line-by-line runtime stream, heartbeats, and structured mission summary.
• Validation Mode (No Training): validates pipeline with --dry-run.
• Force Dataset Redownload: bypasses cached parquet files.
• Abort Active Run: cancels active subprocess tree.

Artifacts

• runtime config: workspace/runtime/deepseek_math_sota.runtime.yaml
• run output root: workspace/runs/math-conjecture-sota
• final adapter: workspace/runs/math-conjecture-sota/final_adapter
• training summary: workspace/runs/math-conjecture-sota/training_summary.json
• post-eval report: workspace/runs/math-conjecture-sota/post_eval_report.json

Notes

• Full training runs on GPU when available and automatically falls back to CPU mode when CUDA is unavailable.
• App handles Gradio copy-button compatibility across versions automatically.